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The value of asymmetry calculation in gait analysis

At Runfisix we use asymmetry calculation routinely during our gait assessments in order to give a high level indication of the injury risk a runner is exposed to at that time. Whilst deep dives into specific metrics and their values can help identify the root cause issues behind movement dysfunction, simple asymmetry values can provide rapid screening feedback when required. 


Asymmetry values can provide rapid screening feedback


The concept of measuring gait parameters from the left and right sides of the body is nothing new in biomechanical assessment. Whenever force output and range of motion is tested in a subject, both the left and right limbs are tested independently and comparisons are drawn. In the case of an injured athlete this is particularly important as the practitioner will commonly aim to compare the injured area side with the equivalent uninjured side to get quick and tangible information on the comparative difference.

So computing the side to side difference as a percentage is merely a formal way of addressing this comparison. However in reality this asymmetry value, sometimes referred to as an asymmetry index, can prove itself to be very powerful in athlete monitoring both for injury prevention and injury recovery. 


The next logical question then is: what levels of asymmetry matter?

At Runfisix we started off by looking at studies that had been published in order to gain a starting point for possible asymmetry percentage criteria. The literature that was available pointed toward the model of a 0-5-10% subdivision. This is the calculation of the left to right side difference (in a given metric) as a percentage and then categorized into one of the divisions of 0-5%, 5-10% or above 10%. Some study results appeared to support the use of this model, whereby the subdivisions were inferred to represent the following:


  • 0-5%: Green light Low injury risk.
  • 5-10%: Yellow light Moderate injury risk.
  • >10%: Red light High injury risk.


Over the years we have tried to put this model to the test, collecting over 2000 datasets with wearable technology to see how a percentage subdivision like this relates to the chance of the runner suffering an injury. And our data and observations appear to support it. In short, those runners that have had green asymmetry values have tended to have very low injury rates whilst those with red values have tended to get injured not long after assessment (in some cases within 2 months). 

Our Runfisix interpretation for the percentage subdivision is this:


  • 0-5%: Green light No need to make an intervention with the runner. The runner can continue running as per their existing training schedule.
  • 5-10%: Yellow light The runner requires monitoring and intervention may be required. The runner can continue their training under the monitoring plan.
  • >10%: Red light High injury risk. The runner requires some sort of immediate help (tbd). The runner should significantly reduce or stop their training schedule.


The model as described above is very simplistic and as with anything in injury prevention there are caveats and complexities. Here are just 3.

Firstly there are many different metrics that can be measured and this can lead to a chaotic pattern of percentages and colored lights for a given runner. So how can this be summarized by just one overall conclusion? The answer lies with the practitioner and the contextual and complementary information they have at their disposal. For example the specific injury history of the runner may mean that recording all but 2 metrics as green is considered as green overall because the practitioner does not see any evidence that the runner has had significant past issues with injuries. 

Secondly there are some metrics that do not fit the 0-5-10 breakdown. As the original work related to this subdivision was based on values of force, the question is then, what about other metrics? At Runfisix we have been working with up to 9 different bilateral metrics and have found that all of them can be placed into a subdivision of either 0-5-10 or 0-10-20 depending on their value magnitude and sensitivity (more on this in a future article). 

The third and final caveat is that asymmetry is not the only way to summarize whether the subject is likely to sustain and injury or not. As we will dive deeper into in a future post, some running injuries are not caused by biomechanical issues but are the product of other factors. Hence we could assess a runner as having dominantly green asymmetry numbers and be at low risk of injury but there could another injury red flag that is far more important in that runner's profile. 


The real power of asymmetry percentages

Where we have seen asymmetry percentages be highly effective is in prevention and recovery monitoring. This requires some understanding of the runner's 'healthy' baseline.

Deviations away from the baseline asymmetry values can be sighted as reasons to investigate further if the runner is heading towards an injury.

Equally a pattern of asymmetry values moving back toward an established baseline can be used as means of ensuring a recovering plan is being effective and informing an eventual return-to-play decision. 

At Runfisix our work is almost exclusively focused on running injury prevention and hence the establishing of a runner's uninjured, healthy baseline data is key. Clearly for ongoing runner monitoring regular follow-up assessments are crucial in order to identify asymmetry trends that are tending away from the baseline. It goes without saying that this kind of runner to practitioner relationship, where regular re-assessment is seen as standard practice is currently not commonplace and is part of a larger picture of how patient support is provided in general across many disciplines. 


Establishing a runner's uninjured, healthy baseline is key


In future case studies on this site we will aim to highlight the benefits of long term relationships with runners and regular re-assessment in terms of helping them stay injury free over long periods of time.

  • Feb 04, 2019
  • Category: Articles
  • Comments: 0
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